Turn lane configuration

ABSTRACT

Systems, methods, and apparatuses are described for determining turn lane data from probe data. Probe data for an intersection including locations and headings is identified. The probe data for the intersection is divided into a first trace and a second trace based on locations. The difference in the headings, heading change data, is calculated. From the heading change data, at least two local extrema points are identified. A characteristic of the turning lane is determined based on the at least two local extrema points.

This application is a continuation under 37 C.F.R. § 1.53(b) and 35U.S.C. § 120 of U.S. patent application Ser. No. 14/640,666 filed Mar.6, 2015 which is incorporated by reference in its entirety.

FIELD

The following disclosure relates to turn lane configuration, or moreparticularly, to the identification of the presence or absence of aturning lane, a starting location of a turn lane, or a quantity of turnlanes from probe data.

BACKGROUND

A geographic database or a road database may include information abouttowns, cities, addresses, and the road network that connects thelocations. The road network may be defined by road links assigned togeographic locations. Other information about the roads may also bestored such turn restrictions, speed limits, stop sign locations, andtraffic signal locations.

Some techniques for collecting information on roads involves manuallyentering the data into a computer. For example, the existence of turninglanes may be observed by observing videos of traffic. That is, a humantechnician may watch videos of vehicles traveling roads and marking theobserved turn lanes on a map. Alternatively, a human technician may logthe turn lanes while collecting map data in a vehicle. That is, duringdata collection, turn lanes observed in real life are marked on a map.Both of these processes is labor intensive, time consuming, andexpensive. Challenges remain in automatic and efficient techniques forautomating turn lane identification and coding.

SUMMARY

In one embodiment, systems, methods, and apparatuses are described fordetermining turn lane data from probe data. Probe data for anintersection including locations and headings is identified. The probedata for the intersection is divided into a first trace and a secondtrace based on locations. The difference in the headings, heading changedata, is calculated. From the heading change data, at least two localextrema points are identified. A characteristic of the turning lane isdetermined based on the at least two local extrema points.

In one embodiment, probe data including location values and headingvalues is identified for an intersection. Heading change data iscalculated based on heading values in the probe data, and at least twolocal maxima of the heading change data are calculated. A characteristicof the turn lane is determined based on the at least two local maxima.

In one embodiment, probe data for an intersection is identified anddivided into at least one turn trace and at least one through tracebased on the locations in the probe data. Heading change data iscalculated as a difference between the headings of the turn trace andthe headings of the through trace. At least two local maxima of theheading change data are identified. From the local maxima, a number ofturning lanes for the intersection is determined.

BRIEF DESCRIPTION OF THE DRAWINGS

Exemplary embodiments are described herein with reference to thefollowing drawings.

FIG. 1 illustrates an example system for identifying turn lanes.

FIG. 2 illustrates a flowchart for identifying a turn lane.

FIG. 3 illustrates an example intersection.

FIG. 4 illustrates an example probe data for an intersection.

FIG. 5 illustrates an example of filtering probe data.

FIG. 6 illustrates an example moving average of heading change values.

FIG. 7 illustrates example shoulder points for heading change values.

FIG. 8 illustrates an example set of turn lanes.

FIG. 9 illustrates an example classification of data for identifyingturn lanes.

FIG. 10 illustrates another example classification of data foridentifying turn lanes.

FIG. 11 illustrates an example stop line.

FIG. 12 illustrates an example data for the stop line of FIG. 11.

FIG. 13 illustrates an example vehicle for collecting probe data or forassisted driving.

FIG. 14 illustrates an example mobile device for the system of FIG. 1.

FIG. 15 illustrates an example flowchart for operation of the mobiledevice of FIG. 14.

FIG. 16 illustrates an example server of the system of FIG. 1.

DETAILED DESCRIPTION

The term turn lane data (TLD) or turn lane designation (TLD) refers to acollection of data describing turn lanes for a road network. A turn laneis a lane, or a particular portion of a road, that is designated to makea turn. The turn lane may extend from one road on the road network toanother road on the road network. The turn lane may be a dedicated lanethat allows vehicles to make a turn without disrupting traffic flow. Aturn lane may be a left or right turn lane. The collection of datadescribing a turn lane may include the location of the turn lane, theentry point of the turn lane, and the exit point of the turn. Thecollection of data describing turn lanes may include a quantity of turnlanes.

The following embodiments automatically determine the TLD values byfinding traces for turns and for through traffic from archived probedata. The determination is achieved by examining the heading change ofthe turn traces with respect to the through traces. This techniqueimproves the speed of the coding process of defining the TLD, and thistechnique is also more reliable.

A geographic database or map database may be updated based on the TLD.The TLD may be utilized in routing and advising a driver when to enter aturn lane. The TLD may be used in autonomous or assisted driving insteering a vehicle in a turn lane.

FIG. 1 illustrates an example system 120 for identifying turn lanes. Thesystem 120 includes a developer system 121, one or more mobile devices122, a workstation 128, and a network 127. Additional, different, orfewer components may be provided. For example, many mobile devices 122and/or workstations 128 connect with the network 127. The developersystem 121 includes a server 125 and one or more databases. The database123 may be a geographic database including road links or segments.

The mobile device 122 may be considered a probe. The mobile device 122may receive sensor data or probe data related to the operation ofvehicle 124 from the sensor 126, which is shown external to the mobiledevice 122 but may also be integrated with the mobile device 122. In oneexample, the sensor data or probe data includes location data (e.g.,geographic coordinates) from a global positioning system (GPS). Thelocation data may be analyzed to determine the speed, acceleration, orchange in acceleration of the vehicle 124. The sensor 126 may include aninertial sensor, a position sensor, a magnetic sensor or another sensor.

In one example, the probe data is analyzed to determine heading data.For example, the server 125 or the mobile device 122 calculates an anglebetween two consecutive probe points. For example, between probe point(X1, Y1) and probe point (X2, Y2), an angle for the heading may be thearctangent of (X2-X1)/(Y2-Y1) or the arctangent of (Y2-Y1)/(X2-X1). Inanother example, the probe data includes measured heading values, forexample, from a magnetic sensor or an inertial sensor. The heading maybe a quantity of degrees measured from an axis such as the horizontalaxis or x-axis or the vertical axis or y-axis. Thus, the probe data mayinclude location values paired with heading values.

FIG. 2 illustrates a flowchart for identifying a turn lane. Additional,different, or fewer acts may be included. At act S101, the server 125 orthe mobile device 122 may identify probe data for an intersection. Forexample, a geographic location or an intersection location is used toselect a set of probe data. The intersection may be defined by fourcoordinate points, forming a quadrilateral. The intersection may bedefined by the intersection of two or more road segments, which may beidentified by road segment identifier values.

At act S103, the server 125 or the mobile device 122 may be configuredto divide the probe data for the intersection into at least one firsttype of trace (e.g., turn trace) and at least one second type of trace(e.g., through trace) based on the locations in the probe data. Theprobe data may be divided into sets of probe points collected by theprobe (e.g., mobile device 122) as the vehicle traverses theintersection. The probe data may be categorized or divided based onwhether the vehicle is on the same road segment after the intersectionas before the intersection. The term downstream may refer to after theintersection in the direction of movement of the vehicle. The termupstream may refer to before the intersection in the direction ofmovement of the vehicle. When the vehicle stays on the same roadsegment, the set of probe data is designated as a through trace. Whenthe vehicle turns onto another road segment, the set of probe data isdesignated as a turn trace.

At act S105, the server 125 or the mobile device 122 calculates headingchange data as a difference between the headings of the turn trace andthe headings of the through trace. Consider an example in which the turntrace includes a sequence of headings [A1, A2, A3, A4] and the throughtrace includes a sequence of headings [B1, B2, B3, B4]. Thus, theheading values are matched by sequence in an array. The heading changedata is calculated as [A1-B1, A2-B2, A3-B3, A4-B4]. Alternatively, themobile device 122 or the server 125 may match points in the turn traceto corresponding points in the through trace based on location. Forexample, points in the turn trace are matched with points in the throughtrace that are closest together in the direction of travel of the roadsegment.

Alternatively, the intersection may be a T-shaped intersection. In aT-shaped intersection all of the lanes are turning lanes. There are nothrough lanes or through traces. In this example, act S103 may beomitted, and the server 125 or mobile device 122 is configured toidentify the turn trace. Further, in act S105, the server 125 or themobile device 122 may identify link geometry for the intersection. Thelink geometry includes locations for the road segment or a lane of theroad segment. Headings for the link geometry are calculated in the samemanner that heading values are calculated from the probe data. Theheading change data is calculated as a difference between the headingvalues in the probe data and heading values derived from the linkgeometry.

At act S107, the server 125 or the mobile device 122 calculates at leasttwo local extrema of the heading change data. The local extrema may belocal maxima, local minima, or a combination of at least one maximum andat least one minimum. The local extrema may be located by comparingadjacent heading change values. When a point is larger than a precedingpoint and a subsequent point, the point is designated as a localmaximum. When a point is smaller than a preceding point and a subsequentpoint, the point is designated as a local minimum. In other examples, aderivative of the heading change data is calculated, and when thederivative equals zero or within a predetermine range to zero, the pointis considered a local extrema. Nearby points may be analyzed todistinguish between minima and maxima.

At act S109, the server 125 or the mobile device 122 determines acharacteristic of a turn lane for the intersection based on the at leasttwo local extrema. The characteristic may be a number of turn lanes orlocations of the turn lanes. In one example, the local maxima may beplotted against a statistical parameter. The statistical parameter maybe a standard deviation, a variance, or a moving average of the headingchange values or other the original heading values. The plot may besegmented to identify regions that correspond to different quantities ofturn lanes. In another example, the local maxima may be classified by amachine learning algorithm. The machine learning algorithm may beBayesian, vector classification, neural network, fuzzy network, or othertechniques.

The mobile device 122 may be a personal navigation device (“PND”), aportable navigation device smart phone, a mobile phone, a personaldigital assistant (“PDA”), a tablet computer, a notebook computer,and/or any other known or later developed mobile device or personalcomputer. Non-limiting embodiments of navigation devices may alsoinclude relational database service devices, mobile phone devices, orcar navigation devices.

The developer system 121, the workstation 128, and the mobile device 122are coupled with the network 127. The phrase “coupled with” is definedto mean directly connected to or indirectly connected through one ormore intermediate components. Such intermediate components may includehardware and/or software-based components.

The computing resources may be divided between the server 125 and themobile device 122. In some embodiments, the server 125 performs amajority of the processing for calculating the vehicle confidence valueand the comparison with the confidence threshold. In other embodiments,the mobile device 122 or the workstation 128 performs a majority of theprocessing. In addition, the processing is divided substantially evenlybetween the server 125 and the mobile device 122 or workstation 128.

The communication between the mobile device 122 and the server 125through the network 127 may be radio or wireless communication such ascellular, the family of protocols known as WiFi or IEEE 802.11, thefamily of protocols known as Bluetooth, or another protocol. Thecellular technologies may be analog advanced mobile phone system (AMPS),the global system for mobile communication (GSM), third generationpartnership project (3GPP), code division multiple access (CDMA),personal handy-phone system (PHS), and 4G or long term evolution (LTE)standards, or another protocol.

FIG. 3 illustrates an example intersection with probe points 131,sidewalks or medians 132, a through trace 133, a turn trace 134, a startline 135, and a stop line 137. Additional or different features may beincluded.

The probe point 131 may be defined as a set of data that minimallyincludes latitude, longitude, and a probe identification number for avehicle. The probe identification number may include an identificationvalue for the vehicle acting as the probe and collecting the probe dataand may include an identification value for an individual point. Theprobe point 131 may also include heading, a timestamp, and speed. Theheading and speed may be measured or calculated from the latitude andlongitude values.

The aggregate probe data may include sets of probe points collected frommultiple vehicles for a period of time. The probe points may becollected by an application running on the mobile device 122. Forexample, the probe points may be collected by mobile phone withoutintervention by the user. The mobile application may be mapping orrouting application or a traffic application. The period of time may beany period of time such as a day, week, month, years, or longer timeperiods. The period of time may be defined by the volume of data. Forexample, the aggregate probe data for an intersection may include apredetermined number of probe points (e.g., 100, 1000, 10000 or anothervalue).

A trace may be a set of probe points in the order of time. That is, asthe probe travels through the intersection, the probe collects asequence of probe points ordered in time. An example trace may be (x1,y1, h1, . . . , t1), (x2, y2, h2, . . . t2), . . . for one vehicle. (x,y, h, . . . t) are latitude, longitude, heading, . . . , and timerespectively.

A turn trace 134 may be a trace that starts near the upstream side ofthe intersection or upstream of the intersection and ends near thedownstream side of the intersection or downstream of the intersectionafter making a turn. Thus, a turn trace 134 is generated when thevehicle collecting the trace enters the intersection and makes a turn.The turn trace 134 may be defined as a traversal in which the vehiclechanges road segment in the intersection.

A through trace 133 may be defined as a trace that starts near theupstream side of the intersection or upstream of the intersection andends near the downstream side of the intersection or downstream of theintersection by traveling substantially straight through theintersection. Thus, a through trace 133 is generated when the vehiclecollecting the trace enters the intersection and exits the intersectionon the same road segment or without substantially changing direction.

A trace start line 135 may be defined as a line indicative of the startof either a turn trace or a through trace. A trace end line 136 may bedefined as a line indicative of an end of either a turn trace or athrough trace. A stop line 137 may be a line indicative of a locationclose to the intersection where a vehicle driven towards theintersection stops during red traffic signal.

FIG. 4 illustrates example probe data for traces of an intersection. Achart 140 shows the probe data. A horizontal axis or x-axis illustratesa turn direction and a vertical axis or y-axis illustrates an initialdirection. Thus, the probe data in rectangle 141 are probes thattraveled along the initial direction then turned onto the turndirection, which is the turn trace. The data outside of the rectanglerepresents probes that continuation to travel along the initialdirection. In this example, the turn direction is north-east and theinitial direction is north-south. Any others directions can be processedin the same way. The turn direction may be perpendicular to the initialdirection, but other angles are possible. The traces for archived probedata or historical probe data are analyzed to determine the presence orabsence of turn lanes, the number of turn lanes, and the start locationof turn lanes. This data may be referred to as a TLD.

Other factors lead to variances in the data. All of the positions forthe probe data in the rectangle 141 were collected by vehicles travelingin the same lane. However, the variances or spatial distribution may becaused by differences in the lateral direction of different vehicles atthe time of driving on the road. That is, the lane is larger than thevehicle so vehicles may choose slightly different paths. Also, GPSerrors contribute to the spatial distribution. Therefore, thedistribution of the probe data set spans certain buffer area on eitherside of the road with concentration on lane centers. Probe headings arealso affected similarly.

The archived probe data are used to create traces. For each studied turnin an intersection, two sets of traces are created, one for the turn(i.e. turn traces) and the other for the through traffic (i.e. throughtraces). An average heading value at every unit distance (e.g. onemeter) is derived for both the turn trace set and through trace set. Adifference between the average headings of the two sets is computed ateach unit distance. Calculating the difference in this way provides theheading change for the turn with respect to that of the through trace.In this way the heading change values are normalized. The pattern of theheading change indicates the presence or absence of turn lanes, numberof turn lanes and start location of turn lanes.

Until a vehicle from a turn trace reaches the actual turn, the headingof the vehicle is similar to that of the through traffic because bothtraces follow the same direction. As the vehicle approaches turn lane,the heading begins to deviate from that of the through traffic. Themagnitude of the deviation varies depending upon the location of thevehicle with respect to the turn lane geometry. As the vehicleapproaches the stop line, the vehicle may realign with the direction ofthrough traffic and therefore the deviation may reduce to zero or nearzero. By analyzing the pattern of this normalized deviation (i.e. theheading difference between the two traces), it is possible to determinethe TLD values.

As shown in FIG. 4, the archive probe data may be limited geographicallyand temporally. The archived probe data may be collected by mobiledevices 122 over time. The mobile devices 122 may include datacollection vehicles, mobile phones, or navigation devices. A subset ofthe archived probe data may be selected according to a period of time.For example, the last year of data or the last week of data may be used.A user input may define the amount of data needed to get reliableresults. In the example of FIG. 4, two years of data are illustrated.

The archived probe data may be limited by a suitable spatial boundingrectangle around the intersection. The rectangle is selected to find allthe left turn trace and through trace data for the intersection or astatistically significant portion of the archived probe data to describethe intersection.

The bounding rectangle may be defined by the start line location and thestop line location for the turn trace and the start line location andthe stop line location for the through trace. In one technique, the stopline 137 may be found by using the location of lowest average speed.That is, speed is calculated or measured along the traces. The regionalong the trace with the lowest speed, is selected as the stop line 137.In another technique, the location of the highest density of zero speedpoints that lie near the intersection is selected for the stop line 137.That is, speeds of zero that indicate stopped vehicles are identified.The location with the greatest number of stopped vehicles is designatedas the stop line location. The start line 135 may be set at apredetermined distance from the turn of the intersection. Thepredetermined distance may be 10 meters, 20 meters, or another value.The predetermined distance may be measured from the point at which theturn trace changes to the turn direction. The predetermined distance maybe measured from the node indicative of the intersection of the roadsegments. The lateral sides of the bounding rectangle may be defined bythe width of the road segment or a predetermined distance.

The archived probe data bound by the rectangle defined by the startlines and the stop lines may be clipped. The server 125 or mobile devicemay be configured to identify the stop line 137 and the start line 135based on the probe data, and remove a portion of the probe datadownstream of the stop line and a portion of the probe data upstream ofthe start line.

The mobile device 122 or the server 125 may be configured to calculateheading values from the probe data for the through trace and calculateheading values from the probe data for the turn trace. Equation 1provides one example for calculating average heading values from a givenset of heading values (α₁, α₂, . . . α_(n)), which may be calculatedfrom the probe data or directly measured as part of the probe data. Thenumber of heading values to be averaged is ‘n’. The function ‘a tan 2’is the arctangent function that gives angle in the range (−180degrees≤α≤180 degrees). Alternatively, an arctangent function may beused and the result limited to a specific angle range suitable for turnsin an intersection.

$\begin{matrix}{H = {{atan}\; 2{\left( {\frac{\sum\limits_{j = 1}^{n}{\sin\;\alpha_{j}}}{n},\frac{\sum\limits_{j = 1}^{n}{\cos\;\alpha_{j}}}{n}} \right).}}} & {{Eq}.\mspace{14mu} 1}\end{matrix}$

The mobile device 122 or the server 125 may be configured to calculatethe standard deviation of the average heading values. The standarddeviation may be calculated separately for the through trace and for theturn trace. Equation 2 provides one example of calculating standarddeviation σ_(H) based on Δ(α_(j),H), which is the angle between α_(j)and H.

$\begin{matrix}{\sigma_{H} = \sqrt{\frac{\sum\limits_{j = 1}^{n}{\Delta\left( {\alpha_{j},H} \right)}}{n - 1}}} & {{Eq}.\mspace{14mu} 2}\end{matrix}$

FIG. 5 illustrates an example of filtering probe data. The mobile device122 or the server 125 may be configured to filter the probe data of thethrough trace based on the average of the heading and the standarddeviation of the heading. Using the average heading and standarddeviation, heading values that are far away from the average are removed(e.g., the probe data in rectangles 146 are removed). For example,values more than a predetermined number of standard deviations areremoved. The predetermined number of standard deviations may be onestandard deviation, two standard deviations, three standard deviations,or a fractional value in between.

In one technique, a window may be defined based on the standarddeviation and the average using a certain window size, (e.g., 10,meters, 15 meters or another value) and increment a unit distance (e.g.,0.5 meters, 1 meter, or another value). The heading data for the throughtrace within the moving window are averaged to calculate a movingaverage value. The window is incremented by the unit distance andanother moving average value is calculated. The process begins when thewindow borders the start lines and repeats until the widow borders thestop line.

The mobile device 122 or the server 125 may be configured to filter theprobe data of the turn trace based on the average of the heading and thestandard deviation of the heading of the turn trace. Using the averageheading and standard deviation, heading values for the turn trace thatare far away from the average are removed (e.g., the probe data inrectangles 146 are removed). For example, values more than apredetermined number of standard deviations are removed. Thepredetermined number of standard deviations may be one standarddeviation, two standard deviations, three standard deviations, or afractional value in between. The number of standard deviations for theturn trace may be greater than the number of standard deviations for thethrough trace.

In one technique, a window for the turn trace may be defined based onthe standard deviation and the average. Using a certain window size,(e.g., 10, meters, 15 meters or another value) and increment a unitdistance (e.g., 0.5 meters, 1 meter, or another value). The window sizeand increment size may be different for the turn trace than for thethrough trace. The heading data for the turn trace within the movingwindow are averaged to calculate a moving average value for the turntrace. The window is incremented by the unit distance and another movingaverage value is calculated. The process begins when the window bordersthe start lines and repeats until the widow borders the stop line. FIG.6 illustrates an example moving average of heading change valuesrepresented by line 149.

The mobile device 122 or the server 125 may be configured to determinethe difference of the turn lane moving average headings and the throughlane moving average headings. This difference is the deviation of turnlane direction with respect to the through lane direction. Thedifference may be referred to as the heading change, which is thenormalized value. Individual values in the moving average of the turntrace may be paired with individual values in the moving average of thethrough trace. The pairs may be matched based on geographic position.

In some examples, because the probe data has errors, calculating theaverage moving window values and the difference between the turn traceand through trace, there remain noises in the heading change data. Suchirregularity can be seen in curve 151 of FIG. 7. In order to reduce theeffects of these errors, the mobile device 122 or server 125 may smooththe heading change data with a filter. In one example, a Kalman type offilter or another type of filter is applied to the heading change datain multiple iterations. A first iteration is applied from the start toend (e.g., start line to end trace line), and a second iteration isapplied from end to the start (e.g., end trace line to start line). Theresults from the iteration may be combined to obtain a smoother headingchange curve, as shown by curve 153.

The mobile device 122 or server 125 may be configured to calculate oneor more maxima and one or more minima from the heading change curve. Themaxima and minima may be calculated from the heading change data or fromthe smoothed heading change curve. Various techniques may be used tocalculate the minima and maxima. In one example, potential points areidentified by calculating a derivative of the heading change data. Whenthe derivative is zero or within a range of zero, a potential minimum ormaximum is identified. The neighboring may be analyzed to distinguishbetween a minimum and a maximum. In another example, the heading changedata is analyzed directed to identify a change in direction. When theneighboring points are larger, the point is a local minimum. When theneighboring points are smaller, the point is a local maximum.

FIG. 7 illustrates example local maxima 156 and 157 and a global minimum155 for heading change values. Among the local minima, the one withlowest value is the global minimum 155. The global minimum 155 is theapproximate location where the turn lane begins. The mobile device 122or the server 125 may store the global minimum 155 as the beginning ofthe turn lane in the map database.

The mobile device 122 or the server 125 may calculate distances betweenpairs of adjacent maxima and minima. The distance may be Euclidiandistances based on the location of the points. The maxima and minima maybe divided based on the global minimum. For example, the two longestdistances are identified. One is the largest distance on one side of theglobal minimum point, and the other is the largest distance on the otherside of the global minimum point.

The two local maxima points, from which the two distances are computed,may be identified as shoulder points. Shoulder point 156 is associatedwith the largest distance on one side of the global minimum point 155.Should point 157 is associated with the largest distance on the otherside of the global minimum point 155. A shoulder point that is far fromstop line is a location where vehicle begin to deviate from throughtraffic direction and enter the turn lane. Similarly the shoulder pointclose to the stop line is a location where vehicle on turn lane beginsaligning with the through traffic direction. In some cases, for examplefor a short extent of a turn lane, there may not be one or the othershoulder point. Similarly, there may not be any local minima point whenthere is no turn lane.

The mobile device 122 or the server 125 may compute the standarddeviation of the heading change of all the points lying between the twoshoulder points. Similarly compute the distance between the two shoulderpoints. These two quantities and the location of global minimum pointand shoulder points are related to the TLD values.

FIG. 8 illustrates an example set of turn lanes. The mobile device 122or the server 125 may apply the analysis above to a map databaseincluding the set of turn lanes. For example, the global minimum 155indicates where the turn lane begins, the first shoulder point 156indicates the location where some turning vehicles change from throughlane towards the turn lane, and the second shoulder point 157 indicatewhen the turning vehicle are again parallel with through traffic.

FIG. 9 illustrates an example chart 160 for the classification of data161 for identifying turn lanes. The data 161 is the standard deviationof the heading change data plotted against the largest distance betweenlocal maxima. It has been shown that the data 161 tend to cluster basedon how many turning lanes are available. The mobile device 122 or theserver 125 may classify the data 161 based on one or more threshold(e.g., dividing lines 170). It is noted that the arrangement of data 161and lines 170 are not necessarily to scale and are one illustrativeexample. The lines 170 divide the data 161 into three regions. Oneregion may correspond to a single turning lane, another region maycorrespond to two turning lanes, and another region may correspond tothree turn lanes. Thus, a region is selected based on a particulardistance between maxima and standard deviation. Alternatively, one ofthe regions may correspond to no turn lanes.

In one example, the dividing line 170 may correspond to about the 0.4standard deviation value. The intersections with one turn lanes may beclumped together and above the 0.4 standard deviation value and havedistance between the shoulder points less than 135 meters. Therefore, itmay be possible to draw a line at 0.4 standard deviation value todistinguish if an intersection has turn lane or not.

FIG. 10 illustrates another example classification of data foridentifying turn lanes. In FIG. 10, four regions are used (e.g., 0, 1,2, 3 turn lanes), and near the intersection of the regions, indicated bycircle 162, some inconclusive data 163 are illustrated. When a pair ofdistance and standard deviation values is in the circle 162 it may beconcluded that the data is inconclusive for the number of turn lanesincluded.

In another example, a learning algorithm may be used for theclassification. The learning algorithm may be trained based on a set ofknown data. For example, a set of classification vectors may be derivedbased on a set of data for roads in which the number of turn lanes isknown. The number of turn lanes, the calculated largest distance, andthe calculated standard deviation for many roads is a training set ofdata used to define the classification vectors. The above procedure wasrepeated for a number of intersections with no turn lane, one turn laneand two turn lanes. The distance and the standard deviation data forthose intersections were computed.

Subsequently, when new data is acquired, and the number of lanes isunknown, the mobile device 122 or the server sends the two maxima of thelargest distance and a standard deviation of the heading data to theclassification system. The classification system returns the number ofturning lanes from the classification system.

Apart from the distance and standard deviation, it is also possible toderive other statistical quantities about the heading change values andthe length of the turn, for example accumulated heading change,individual heading changes from one shoulder point to global minimum, orother values. These are additional values to infer the TLD valuescorrectly.

FIGS. 11 and 12 illustrate calculation of an example stop line 172. FIG.12 illustrates a chart 180 of data for the stop line 172. A stop line isa location close to the intersection where the approaching vehicles stopduring red lights. Intuitively, there is a high density of probe pointswith zero speed before the stop line and a low density after it. Thusthe stop line is characterized by an abrupt change of the concentrationof zero-speed probe points. The following procedure may be used tolocate the stop line.

First, all the probe points with zero speed from the trace data areextracted, as shown in FIG. 12. Next, the density distribution ofzero-speed probe points is computed, the maximum point from the densitydistribution curve shown in FIG. 12 is located. Techniques are describedabove for locating the maximum value. The location of this point is anestimated location of the stop line 172.

Various applications may be improved by the addition of accurateautomatically determine TLD values. A map or geographic database isimproved because the number of turning lanes and location of the turninglanes allows drivers to anticipate maneuvers. In addition, the routesderived from these databases may be more precise. For example, the routemay include a routing instruct stating, “proceed left to the secondturning lane” or “begin entering the turning lane in 100 meters” or“proceed to the next turning lane.” Driving assistance may also beprovided based on the TLD values, which is discussed in more detailbelow.

FIG. 13 illustrates example vehicles that may be used for collectingprobe data or may be used as assisted driving vehicles. An array ofsensors 111 may include any combination of a brake sensor, a steeringsensor, an environment sensor, a vehicle sensor, an optical sensor, andan inertial sensor. Additional, different, or fewer sensors may be used.The brake sensor may be a brake pedal sensor that detects displacementof the brake pedal of the vehicle. The brake sensor may detect theactuation of the brake pads near the wheel of the vehicle. The brakesensor may be a circuit that detects operation of the brakes through ananti-lock brake system. The steering sensor may be a steering wheelsensor that detects movement of the steering wheel of the vehicle. Thesteering sensor 73 may detect the angle of the steering wheel. Thesteering sensor may detect the angle of the front wheel of the vehicle.The environment sensor may detect the environment of the vehicle. Theenvironment sensor may include a weather sensor such as a thermometer,barometer, or a rain sensor. The rain sensor may detect the movement ofwindshield wipers. The vehicle sensor may detect an operation of thevehicle. The vehicle sensor may include a throttle sensor that measuresa position of a throttle of the engine or a position of an acceleratorpedal, a speedometer sensor, or a tachometer sensor. The vehicle sensormay detect a malfunction of the vehicle. For example, the vehicle sensormay be a tire pressure sensor. The optical sensor may include a camera,a LiDAR device, a proximity sensor, or another sensor configured todetect distances to nearby objects or when a nearby object exists. Theoptical sensor may send a signal that reflects off another object and isdetected by the optical sensor. The inertial sensor may include aninertial measurement unit (IMU) including one or more of anaccelerometer, a gyroscope, and a magnetic sensor. The inertial sensormay generate data indicative of the acceleration, deceleration,rotational acceleration, and rotation deceleration experienced by thevehicle.

The vehicles 124 may be assisted driving vehicles. Assisted drivingvehicles include autonomous vehicles, highly assisted driving (HAD), andadvanced driving assistance systems (ADAS). Any of these assisteddriving systems may be incorporated into mobile device 122.Alternatively, an assisted driving device may be included in the vehicle124. The assisted driving device may include memory, a processor, andsystems to communicate with the mobile device 122.

The term autonomous vehicle may refer to a self-driving or driverlessmode in which no passengers are required to be on board to operate thevehicle. An autonomous vehicle may be referred to as a robot vehicle oran automated vehicle. The autonomous vehicle may include passengers, butno driver is necessary. These autonomous vehicles may park themselves ormove cargo between locations without a human operator. Autonomousvehicles may include multiple modes and transition between the modes.The autonomous vehicle may steer, brake, or accelerate the vehicle basedon the turn lane data.

A highly assisted driving (HAD) vehicle may refer to a vehicle that doesnot completely replace the human operator. Instead, in a highly assisteddriving mode, the vehicle may perform some driving functions and thehuman operator may perform some driving functions. Vehicles may also bedriven in a manual mode in which the human operator exercises a degreeof control over the movement of the vehicle. The vehicles may alsoinclude a completely driverless mode. Other levels of automation arepossible. The HAD vehicle may control the vehicle through steering orbraking in response to the turn lane data.

Similarly, ADAS vehicles include one or more partially automated systemsin which the vehicle alerts the driver. The features are designed toavoid collisions automatically. Features may include adaptive cruisecontrol, automate braking, or steering adjustments to keep the driver inthe correct lane. ADAS vehicles may issue controls for these feature inresponse to the turn lane data.

The vehicles 124 may be equipped with a mobile device 122 and a sensorarray 111 including one or a combination of the sensors described withrespect to FIG. 4. The sensors may include a camera. One example camera115 a is mounted on the top of the vehicle and has a 360 degree field ofview, and another type of camera 115 b is mounted on a front, rear, orside of the vehicle 124 and has a wide angle view less than a 360 fieldof view. Cameras may be omitted.

The mobile device 122 or server 125 may be configured to analyze theimages or video collected by a camera. The images may be processed usingcomputer vision to identify one or more nearby objects. Example imageprocessing techniques include vector classification, edge detection, andfeature extraction. In one example, the camera captures images of theroad surface in front of the vehicle, and the mobile device 122 or theserver 125 may determine when any foreign object is present on the roadsurface or another vehicle is close to the vehicle 124.

The mobile device 122 may be a personal device such as a mobile phoneequipped with position circuitry (e.g., global positioning system (GPS))and an inertial measurement unit (IMU). The mobile device 122 may be aspecialized device (e.g., not a mobile phone) mounted or otherwiseassociated with the vehicle 124 and similarly equipped with positioncircuitry and an IMU. Additional, different, or fewer components may beincluded.

FIG. 14 illustrates an exemplary mobile device 122 of the system ofFIG. 1. The mobile device 122 includes a processor 200, a memory 204, aninput device 203, a communication interface 205, position circuitry 207,and a display 211. Additional, different, or fewer components arepossible for the mobile device/personal computer 122. FIG. 15illustrates an example flowchart for operation of the mobile device ofFIG. 14. The acts of FIG. 14 may be performed by the mobile device 122,an advanced driving assistance system (ADAS), a HAD device or anautonomous vehicle, any of which may be referred to as an assisteddriving system. The acts may be applied in a different order. Acts maybe omitted or repeated. Additional acts may be added.

At act S201, the processor 200 sends a request for a route, map data, ora driving maneuver. The request for a route may be based on adestination location entered by through the input device 203 and anorigin destination either entered by the input device 203 or determinedas a current location of the mobile device by the position circuitry207. The request for map data may similar be based on a geographicregion around the current location or an entered location. The drivingmaneuver may also be based on a destination that the assisted drivingsystem is providing driving commands to reach.

At act S203, the processor 200 or communication interface 205 receivesturn lane data in response to the request. The turn lane data maydescribe the location of turn lanes such as the beginning of the turnlane, the average entry point of the turn lane, and the ending of theturn lane. The turn lane data may describe a quantity of turn lanes.

At act S205, the processor 200 may apply the turn lane data. Forexample, the processor 200 may instruct the display 211 to present a mapinclude the turn lane data such as the location of the turn lane(s) orthe number of turn lanes. The processor 200 may generate a messageregarding the turn lane. For example, the display 211 may present amessage when the turn lane begins or warn of an approaching turn lane.The display 211 may present a message that describes the number of turnlanes at the intersection and the destination road correspond to each ofthe turn lanes. The processor 200 may apply the turn lane data bygenerating a driving command. The driving command may be a steeringcommand to steer the vehicle 124 into a turn lane based on the locationof the turn lane in the turn lane data. The driving command may be aturn signal based on the location of an upcoming turn lane. The drivingcommand may be a braking signal to slow down for the turn lane.

FIG. 16 illustrates an example network device (e.g., server 125) of thesystem of FIG. 1. The server 125 includes a processor 300, acommunication interface 305, and a memory 301. The server 125 may becoupled to a database 123 and a workstation 128. The workstation 128 maybe used as an input device for the server 125 for entering values forthe thresholds and predetermined distances for calculating the turn lanedata. In addition, the communication interface 305 is an input devicefor the server 125. In certain embodiments, the communication interface305 may receive data indicative of user inputs made via the workstation128 or the mobile device 122.

The road link data records of the database 123 may be associated withattributes of or about the roads such as, for example, geographiccoordinates, street names, address ranges, speed limits, turnrestrictions at intersections, and/or other navigation relatedattributes (e.g., one or more of the road segments is part of a highwayor tollway, the location of stop signs and/or stoplights along the roadsegments), as well as points of interest (POIs), such as gasolinestations, hotels, restaurants, museums, stadiums, offices, automobiledealerships, auto repair shops, buildings, stores, parks, etc. The nodedata records may be associated with attributes (e.g., about theintersections) such as, for example, geographic coordinates, streetnames, address ranges, speed limits, turn restrictions at intersections,and other navigation related attributes, as well as POIs such as, forexample, gasoline stations, hotels, restaurants, museums, stadiums,offices, automobile dealerships, auto repair shops, buildings, stores,parks, etc. The geographic data may additionally or alternativelyinclude other data records such as, for example, POI data records,topographical data records, cartographic data records, routing data, andmaneuver data.

The databases 123 may be maintained by one or more map developers (e.g.,the first company and/or the second company). A map developer collectsgeographic data to generate and enhance the database. There aredifferent ways used by the map developer to collect data. These waysinclude obtaining data from other sources such as municipalities orrespective geographic authorities. In addition, the map developer mayemploy field personnel (e.g., the employees at the first company and/orthe second company) to travel by vehicle along roads throughout thegeographic region to observe features and/or record information aboutthe features. Also, remote sensing such as, for example, aerial orsatellite photography may be used.

The database 123 may be master geographic databases stored in a formatthat facilitates updating, maintenance, and development. For example, amaster geographic database or data in the master geographic database isin an Oracle spatial format or other spatial format, such as fordevelopment or production purposes. The Oracle spatial format ordevelopment/production database may be compiled into a delivery formatsuch as a geographic data file (GDF) format. The data in the productionand/or delivery formats may be compiled or further compiled to formgeographic database products or databases that may be used in end usernavigation devices or systems.

For example, geographic data is compiled (such as into a physicalstorage format (PSF) format) to organize and/or configure the data forperforming navigation-related functions and/or services, such as routecalculation, route guidance, map display, speed calculation, distanceand travel time functions, and other functions, by a navigation device.The navigation-related functions may correspond to vehicle navigation,pedestrian navigation, or other types of navigation. The compilation toproduce the end user databases may be performed by a party or entityseparate from the map developer. For example, a customer of the mapdeveloper, such as a navigation device developer or other end userdevice developer, may perform compilation on a received geographicdatabase in a delivery format to produce one or more compiled navigationdatabases.

The workstation 128 may be a general purpose computer includingprogramming specialized for providing input to the server 125. Forexample, the workstation 128 may provide settings for the server 125.The settings may include the bounding rectangle, distance thresholds,distance for defining the start line, the window and increment size forthe moving average window, or statistic thresholds described above forthe calculation of the turn lane data. The workstation 128 may includeat least a memory, a processor, and a communication interface.

The bounding rectangle for the intersection may be defined based thefunctional class of the associated road segment for the turning lane.The functional class of the road segment may be described as a numericalvalue (e.g., 1, 2, 3, 4, and 5). Functional class 1 may be highwayswhile functional class 5 may be small streets.

One example of a simple system includes the functional classificationmaintained by the United States Federal Highway administration. Thesimple system includes arterial roads, collector roads, and local roads.The functional classifications of roads balance between accessibilityand speed. An arterial road has low accessibility but is the fastestmode of travel between two points. Arterial roads are typically used forlong distance travel. Collector roads connect arterial roads to localroads. Collector roads are more accessible and slower than arterialroads. Local roads are accessible to individual homes and business.Local roads are the most accessible and slowest type of road.

An example of a complex functional classification system is the urbanclassification system. Interstates include high speed and controlledaccess roads that span long distances. The arterial roads are dividedinto principle arteries and minor arteries according to size. Thecollector roads are divided into major collectors and minor collectorsaccording to size. Another example functional classification systemdivides long distance roads by type of road or the entity in control ofthe highway. The functional classification system includes interstateexpressways, federal highways, state highways, local highways, and localaccess roads. Another functional classification system uses the highwaytag system in the Open Street Map (OSM) system. The functionalclassification includes motorways, trunk roads, primary roads, secondaryroads, tertiary roads, and residential roads.

The computing device processor 200 and/or the server processor 300 mayinclude a general processor, digital signal processor, an applicationspecific integrated circuit (ASIC), field programmable gate array(FPGA), analog circuit, digital circuit, combinations thereof, or othernow known or later developed processor. The mobile device processor 200and/or the server processor 300 may be a single device or combinationsof devices, such as associated with a network, distributed processing,or cloud computing. The computing device processor 200 and/or the serverprocessor 300 may also be configured to cause an apparatus to at leastperform at least one of methods described above.

The memory 204 and/or memory 301 may be a volatile memory or anon-volatile memory. The memory 204 and/or memory 301 may include one ormore of a read only memory (ROM), random access memory (RAM), a flashmemory, an electronic erasable program read only memory (EEPROM), orother type of memory. The memory 204 and/or memory 301 may be removablefrom the mobile device 122, such as a secure digital (SD) memory card.

The communication interface 205 and/or communication interface 305 mayinclude any operable connection. An operable connection may be one inwhich signals, physical communications, and/or logical communicationsmay be sent and/or received. An operable connection may include aphysical interface, an electrical interface, and/or a data interface.The communication interface 205 and/or communication interface 305provides for wireless and/or wired communications in any now known orlater developed format.

In the above described embodiments, the network 127 may include wirednetworks, wireless networks, or combinations thereof. The wirelessnetwork may be a cellular telephone network, an 802.11, 802.16, 802.20,or WiMax network. Further, the network 127 may be a public network, suchas the Internet, a private network, such as an intranet, or combinationsthereof, and may utilize a variety of networking protocols now availableor later developed including, but not limited to TCP/IP based networkingprotocols.

While the non-transitory computer-readable medium is described to be asingle medium, the term “computer-readable medium” includes a singlemedium or multiple media, such as a centralized or distributed database,and/or associated caches and servers that store one or more sets ofinstructions. The term “computer-readable medium” shall also include anymedium that is capable of storing, encoding or carrying a set ofinstructions for execution by a processor or that cause a computersystem to perform any one or more of the methods or operations disclosedherein.

In a particular non-limiting, exemplary embodiment, thecomputer-readable medium can include a solid-state memory such as amemory card or other package that houses one or more non-volatileread-only memories. Further, the computer-readable medium can be arandom access memory or other volatile re-writable memory. Additionally,the computer-readable medium can include a magneto-optical or opticalmedium, such as a disk or tapes or other storage device to capturecarrier wave signals such as a signal communicated over a transmissionmedium. A digital file attachment to an e-mail or other self-containedinformation archive or set of archives may be considered a distributionmedium that is a tangible storage medium. Accordingly, the disclosure isconsidered to include any one or more of a computer-readable medium or adistribution medium and other equivalents and successor media, in whichdata or instructions may be stored.

In an alternative embodiment, dedicated hardware implementations, suchas application specific integrated circuits, programmable logic arraysand other hardware devices, can be constructed to implement one or moreof the methods described herein. Applications that may include theapparatus and systems of various embodiments can broadly include avariety of electronic and computer systems. One or more embodimentsdescribed herein may implement functions using two or more specificinterconnected hardware modules or devices with related control and datasignals that can be communicated between and through the modules, or asportions of an application-specific integrated circuit. Accordingly, thepresent system encompasses software, firmware, and hardwareimplementations.

In accordance with various embodiments of the present disclosure, themethods described herein may be implemented by software programsexecutable by a computer system. Further, in an exemplary, non-limitedembodiment, implementations can include distributed processing,component/object distributed processing, and parallel processing.Alternatively, virtual computer system processing can be constructed toimplement one or more of the methods or functionality as describedherein.

Although the present specification describes components and functionsthat may be implemented in particular embodiments with reference toparticular standards and protocols, the invention is not limited to suchstandards and protocols. For example, standards for Internet and otherpacket switched network transmission (e.g., TCP/IP, UDP/IP, HTML, HTTP,HTTPS) represent examples of the state of the art. Such standards areperiodically superseded by faster or more efficient equivalents havingessentially the same functions. Accordingly, replacement standards andprotocols having the same or similar functions as those disclosed hereinare considered equivalents thereof.

A computer program (also known as a program, software, softwareapplication, script, or code) can be written in any form of programminglanguage, including compiled or interpreted languages, and it can bedeployed in any form, including as a standalone program or as a module,component, subroutine, or other unit suitable for use in a computingenvironment. A computer program does not necessarily correspond to afile in a file system. A program can be stored in a portion of a filethat holds other programs or data (e.g., one or more scripts stored in amarkup language document), in a single file dedicated to the program inquestion, or in multiple coordinated files (e.g., files that store oneor more modules, sub programs, or portions of code). A computer programcan be deployed to be executed on one computer or on multiple computersthat are located at one site or distributed across multiple sites andinterconnected by a communication network.

The processes and logic flows described in this specification can beperformed by one or more programmable processors executing one or morecomputer programs to perform functions by operating on input data andgenerating output. The processes and logic flows can also be performedby, and apparatus can also be implemented as, special purpose logiccircuitry, e.g., an FPGA (field programmable gate array) or an ASIC(application specific integrated circuit).

As used in this application, the term “circuitry” or “circuit” refers toall of the following: (a) hardware-only circuit implementations (such asimplementations in only analog and/or digital circuitry) and (b) tocombinations of circuits and software (and/or firmware), such as (asapplicable): (i) to a combination of processor(s) or (ii) to portions ofprocessor(s)/software (including digital signal processor(s)), software,and memory(ies) that work together to cause an apparatus, such as amobile phone or server, to perform various functions) and (c) tocircuits, such as a microprocessor(s) or a portion of amicroprocessor(s), that require software or firmware for operation, evenif the software or firmware is not physically present.

This definition of “circuitry” applies to all uses of this term in thisapplication, including in any claims. As a further example, as used inthis application, the term “circuitry” would also cover animplementation of merely a processor (or multiple processors) or portionof a processor and its (or their) accompanying software and/or firmware.The term “circuitry” would also cover, for example and if applicable tothe particular claim element, a baseband integrated circuit orapplications processor integrated circuit for a mobile phone or asimilar integrated circuit in server, a cellular network device, orother network device.

Processors suitable for the execution of a computer program include, byway of example, both general and special purpose microprocessors, andanyone or more processors of any kind of digital computer. Generally, aprocessor receives instructions and data from a read only memory or arandom access memory or both. The essential elements of a computer are aprocessor for performing instructions and one or more memory devices forstoring instructions and data. Generally, a computer also includes, orbe operatively coupled to receive data from or transfer data to, orboth, one or more mass storage devices for storing data, e.g., magnetic,magneto optical disks, or optical disks. However, a computer need nothave such devices. Moreover, a computer can be embedded in anotherdevice, e.g., a mobile telephone, a personal digital assistant (PDA), amobile audio player, a Global Positioning System (GPS) receiver, to namejust a few. Computer readable media suitable for storing computerprogram instructions and data include all forms of non-volatile memory,media and memory devices, including by way of example semiconductormemory devices, e.g., E PROM, EEPROM, and flash memory devices; magneticdisks, e.g., internal hard disks or removable disks; magneto opticaldisks; and CD ROM and DVD-ROM disks. The processor and the memory can besupplemented by, or incorporated in, special purpose logic circuitry.

To provide for interaction with a user, embodiments of the subjectmatter described in this specification can be implemented on a devicehaving a display, e.g., a CRT (cathode ray tube) or LCD (liquid crystaldisplay) monitor, for displaying information to the user and a keyboardand a pointing device, e.g., a mouse or a trackball, by which the usercan provide input to the computer. Other kinds of devices can be used toprovide for interaction with a user as well; for example, feedbackprovided to the user can be any form of sensory feedback, e.g., visualfeedback, auditory feedback, or tactile feedback; and input from theuser can be received in any form, including acoustic, speech, or tactileinput.

Embodiments of the subject matter described in this specification can beimplemented in a computing system that includes a back end component,e.g., as a data server, or that includes a middleware component, e.g.,an application server, or that includes a front end component, e.g., aclient computer having a graphical user interface or a Web browserthrough which a user can interact with an implementation of the subjectmatter described in this specification, or any combination of one ormore such back end, middleware, or front end components. The componentsof the system can be interconnected by any form or medium of digitaldata communication, e.g., a communication network. Examples ofcommunication networks include a local area network (“LAN”) and a widearea network (“WAN”), e.g., the Internet.

The computing system can include clients and servers. A client andserver are generally remote from each other and typically interactthrough a communication network. The relationship of client and serverarises by virtue of computer programs running on the respectivecomputers and having a client-server relationship to each other.

The illustrations of the embodiments described herein are intended toprovide a general understanding of the structure of the variousembodiments. The illustrations are not intended to serve as a completedescription of all of the elements and features of apparatus and systemsthat utilize the structures or methods described herein. Many otherembodiments may be apparent to those of skill in the art upon reviewingthe disclosure. Other embodiments may be utilized and derived from thedisclosure, such that structural and logical substitutions and changesmay be made without departing from the scope of the disclosure.Additionally, the illustrations are merely representational and may notbe drawn to scale. Certain proportions within the illustrations may beexaggerated, while other proportions may be minimized. Accordingly, thedisclosure and the figures are to be regarded as illustrative ratherthan restrictive.

While this specification contains many specifics, these should not beconstrued as limitations on the scope of the invention or of what may beclaimed, but rather as descriptions of features specific to particularembodiments of the invention. Certain features that are described inthis specification in the context of separate embodiments can also beimplemented in combination in a single embodiment. Conversely, variousfeatures that are described in the context of a single embodiment canalso be implemented in multiple embodiments separately or in anysuitable sub-combination. Moreover, although features may be describedabove as acting in certain combinations and even initially claimed assuch, one or more features from a claimed combination can in some casesbe excised from the combination, and the claimed combination may bedirected to a sub-combination or variation of a sub-combination.

Similarly, while operations are depicted in the drawings and describedherein in a particular order, this should not be understood as requiringthat such operations be performed in the particular order shown or insequential order, or that all illustrated operations be performed, toachieve desirable results. In certain circumstances, multitasking andparallel processing may be advantageous. Moreover, the separation ofvarious system components in the embodiments described above should notbe understood as requiring such separation in all embodiments, and itshould be understood that the described program components and systemscan generally be integrated together in a single software product orpackaged into multiple software products.

It is intended that the foregoing detailed description be regarded asillustrative rather than limiting and that it is understood that thefollowing claims including all equivalents are intended to define thescope of the invention. The claims should not be read as limited to thedescribed order or elements unless stated to that effect. Therefore, allembodiments that come within the scope and spirit of the followingclaims and equivalents thereto are claimed as the invention.

We claim:
 1. A method comprising: receiving sensor data collected by aplurality of sensors in different mobile devices; designating a firstset of the sensor data corresponding to a straight path through anintersection; designating a second set of the sensor data correspondingto a turning path through the intersection; calculating a firstplurality of angles between consecutive pairs of sensor data in thefirst set of the sensor data; calculating a second plurality of anglesbetween consecutive pairs of sensor data in the second set of the sensordata; calculating respective differences between each of the firstplurality of angles and corresponding angles of the second plurality ofangles; and determining a quantity for a plurality of turn lanes for theintersection based on the respective differences between each of thefirst plurality of angles and the corresponding angles of the secondplurality of angles.
 2. The method of claim 1, further comprising:calculating a statistical parameter based on the respective differencesbetween each of the first plurality of angles and the correspondingangles of the second plurality of angles, wherein the quantity for theplurality of turn lanes is based on the statistical parameter.
 3. Themethod of claim 1, further comprising: calculating a derivative of datafor the respective differences between each of the first plurality ofangles and the corresponding angles of the second plurality of angles,wherein the quantity for the plurality of turn lanes is based on thederivative.
 4. The method of claim 1, wherein the intersection isdefined by a quadrilateral of four coordinate points.
 5. The method ofclaim 1, wherein one of the consecutive pairs of sensor data includesprobe point (X1, Y1) and probe point (X2, Y2), and an angle for thefirst plurality of angles is the arctangent of (X2-X1)/(Y2-Y1) or thearctangent of (Y2-Y1)/(X2-X1).
 6. The method of claim 1, wherein theplurality of sensors includes at least one global position system. 7.The method of claim 1, wherein the plurality of sensors includes atleast one magnetic sensor or at least one inertial sensor.
 8. The methodof claim 1, further comprising: determining the straight path when amobile device returns sensor data for positions on a same road beforeand after the intersection; and determining the turning path when themobile device returns sensor data for positions on different roadsbefore and after the intersection.
 9. The method of claim 1, furthercomprising: matching points in the turning path with points in thestraight path that are closest together in a direction of travel,wherein the first plurality of angles and the second plurality of anglescorrespond to the matched points that are closest together in thedirection of travel.
 10. The method of claim 1, further comprising:identifying a stop line and a start line based on the sensor data,wherein the stop line and start line define boundaries of theintersection.
 11. The method of claim 10, further comprising: removing aportion of the sensor data downstream of the stop line; and removing aportion of the sensor data upstream of the start line.
 12. An apparatuscomprising: a memory configured to store sensor data collected by aplurality of sensors in different mobile devices; a controllerconfigured to calculate a first plurality of angles between consecutivepairs of sensor data in a first set of the set of sensor datacorresponding to a straight path through an intersection and configuredto calculate a second plurality of angles between consecutive pairs ofsensor data in a second set of the set of sensor data corresponding to aturning path through the intersection, wherein the controller isconfigured to calculate respective difference between each of the firstplurality of angles and corresponding angles of the second plurality ofangles and a number of turn lanes for the intersection based on therespective differences between each of the first plurality of angles andthe corresponding angles of the second plurality of angles.
 13. Theapparatus of claim 12, wherein the controller is configured to calculatea statistical parameter based on the respective differences between eachof the first plurality of angles and the corresponding angles of thesecond plurality of angles, wherein the number of lanes is based on thestatistical parameter.
 14. The apparatus of claim 12, wherein thecontroller is configured to calculate a derivative of data for therespective differences between each of the first plurality of angles andthe corresponding angles of the second plurality of angles, wherein thenumber of lanes is based on the derivative.
 15. The apparatus of claim12, wherein the intersection is defined by a quadrilateral of fourcoordinate points.
 16. The apparatus of claim 12, wherein one of theconsecutive pairs of sensor data includes probe point (X1, Y1) and probepoint (X2, Y2), and an angle for the first plurality of angles is thearctangent of (X2-X1)/(Y2-Y1) or the arctangent of (Y2-Y1)/(X2-X1). 17.The apparatus of claim 12, wherein the plurality of sensors includes atleast one global position system, at least one magnetic sensor or atleast one inertial sensor.
 18. The apparatus of claim 12, wherein thecontroller is configured to match points in the turning path with pointsin the straight path that are closest together in a direction of travelfor the respective differences between the first plurality of angles andthe second plurality of angles.
 19. An apparatus comprising: at leastone processor; and at least one memory including computer program codefor one or more programs, the at least one memory and the computerprogram code configured to, with the at least one processor, cause theapparatus to at least: receive sensor data collected by a plurality ofglobal positioning systems; designate a first set of the sensor datacorresponding to a straight path through an intersection; designate asecond set of the sensor data corresponding to a turning path throughthe intersection; calculate a first plurality of angles betweenconsecutive pairs of sensor data in the first set of the sensor data;calculate a second plurality of angles between consecutive pairs ofsensor data in the second set of the sensor data; calculate respectivedifferences between each of the first plurality of angles andcorresponding angles of the second plurality of angles; and determine anumber of turn lanes for the intersection based on the respectivedifferences between each of the first plurality of angles and thecorresponding angles of the second plurality of angles.
 20. Theapparatus of claim 19, the at least one memory and the computer programcode configured to, with the at least one processor, cause the apparatusto at least: determining locations for the number of turn lanes for theintersection based on the respective differences between each of thefirst plurality of angles and the corresponding angles of the secondplurality of angles.